Overview

Dataset statistics

Number of variables27
Number of observations53759
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 MiB
Average record size in memory216.0 B

Variable types

Numeric24
Categorical3

Alerts

Cycle is highly overall correlated with RULHigh correlation
Operation Setting 1 is highly overall correlated with Operation Setting 2 and 20 other fieldsHigh correlation
Operation Setting 2 is highly overall correlated with Operation Setting 1 and 18 other fieldsHigh correlation
Operation Setting 3 is highly overall correlated with Operation Setting 1 and 14 other fieldsHigh correlation
RUL is highly overall correlated with CycleHigh correlation
Sensor Measure 1 is highly overall correlated with Operation Setting 1 and 19 other fieldsHigh correlation
Sensor Measure 10 is highly overall correlated with Operation Setting 1 and 22 other fieldsHigh correlation
Sensor Measure 11 is highly overall correlated with Operation Setting 1 and 22 other fieldsHigh correlation
Sensor Measure 12 is highly overall correlated with Operation Setting 1 and 19 other fieldsHigh correlation
Sensor Measure 13 is highly overall correlated with Operation Setting 3 and 11 other fieldsHigh correlation
Sensor Measure 14 is highly overall correlated with Operation Setting 3 and 13 other fieldsHigh correlation
Sensor Measure 15 is highly overall correlated with Operation Setting 1 and 21 other fieldsHigh correlation
Sensor Measure 16 is highly overall correlated with Operation Setting 1 and 19 other fieldsHigh correlation
Sensor Measure 17 is highly overall correlated with Operation Setting 1 and 22 other fieldsHigh correlation
Sensor Measure 18 is highly overall correlated with Operation Setting 1 and 22 other fieldsHigh correlation
Sensor Measure 19 is highly overall correlated with Operation Setting 1 and 16 other fieldsHigh correlation
Sensor Measure 2 is highly overall correlated with Operation Setting 1 and 21 other fieldsHigh correlation
Sensor Measure 20 is highly overall correlated with Operation Setting 1 and 18 other fieldsHigh correlation
Sensor Measure 21 is highly overall correlated with Operation Setting 1 and 18 other fieldsHigh correlation
Sensor Measure 3 is highly overall correlated with Operation Setting 1 and 22 other fieldsHigh correlation
Sensor Measure 4 is highly overall correlated with Operation Setting 1 and 22 other fieldsHigh correlation
Sensor Measure 5 is highly overall correlated with Operation Setting 1 and 18 other fieldsHigh correlation
Sensor Measure 6 is highly overall correlated with Operation Setting 1 and 18 other fieldsHigh correlation
Sensor Measure 7 is highly overall correlated with Operation Setting 1 and 18 other fieldsHigh correlation
Sensor Measure 8 is highly overall correlated with Operation Setting 1 and 21 other fieldsHigh correlation
Sensor Measure 9 is highly overall correlated with Operation Setting 1 and 22 other fieldsHigh correlation
Operation Setting 2 has 4187 (7.8%) zerosZeros

Reproduction

Analysis started2024-06-01 18:40:07.815930
Analysis finished2024-06-01 18:41:35.189659
Duration1 minute and 27.37 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

UnitNumber
Real number (ℝ)

Distinct260
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.08298
Minimum1
Maximum260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:35.321947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q168
median131
Q3195
95-th percentile248
Maximum260
Range259
Interquartile range (IQR)127

Descriptive statistics

Standard deviation74.463862
Coefficient of variation (CV)0.56806658
Kurtosis-1.1565283
Mean131.08298
Median Absolute Deviation (MAD)63
Skewness0.008221439
Sum7046890
Variance5544.8667
MonotonicityIncreasing
2024-06-01T20:41:35.556736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112 378
 
0.7%
88 365
 
0.7%
85 347
 
0.6%
118 344
 
0.6%
31 343
 
0.6%
255 340
 
0.6%
260 316
 
0.6%
257 309
 
0.6%
158 308
 
0.6%
94 306
 
0.6%
Other values (250) 50403
93.8%
ValueCountFrequency (%)
1 149
0.3%
2 269
0.5%
3 206
0.4%
4 235
0.4%
5 154
0.3%
6 175
0.3%
7 174
0.3%
8 151
0.3%
9 199
0.4%
10 184
0.3%
ValueCountFrequency (%)
260 316
0.6%
259 205
0.4%
258 143
0.3%
257 309
0.6%
256 163
0.3%
255 340
0.6%
254 260
0.5%
253 149
0.3%
252 135
 
0.3%
251 266
0.5%

Cycle
Real number (ℝ)

HIGH CORRELATION 

Distinct378
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.15475
Minimum1
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:35.846644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q152
median104
Q3157
95-th percentile231
Maximum378
Range377
Interquartile range (IQR)105

Descriptive statistics

Standard deviation69.180569
Coefficient of variation (CV)0.63378434
Kurtosis-0.22701327
Mean109.15475
Median Absolute Deviation (MAD)52
Skewness0.50086424
Sum5868050
Variance4785.9511
MonotonicityNot monotonic
2024-06-01T20:41:36.038652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 260
 
0.5%
66 260
 
0.5%
96 260
 
0.5%
2 260
 
0.5%
94 260
 
0.5%
93 260
 
0.5%
92 260
 
0.5%
91 260
 
0.5%
90 260
 
0.5%
89 260
 
0.5%
Other values (368) 51159
95.2%
ValueCountFrequency (%)
1 260
0.5%
2 260
0.5%
3 260
0.5%
4 260
0.5%
5 260
0.5%
6 260
0.5%
7 260
0.5%
8 260
0.5%
9 260
0.5%
10 260
0.5%
ValueCountFrequency (%)
378 1
< 0.1%
377 1
< 0.1%
376 1
< 0.1%
375 1
< 0.1%
374 1
< 0.1%
373 1
< 0.1%
372 1
< 0.1%
371 1
< 0.1%
370 1
< 0.1%
369 1
< 0.1%

Operation Setting 1
Real number (ℝ)

HIGH CORRELATION 

Distinct536
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.998407
Minimum0
Maximum42.008
Zeros145
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:36.183675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q110.0046
median25.0013
Q341.998
95-th percentile42.006
Maximum42.008
Range42.008
Interquartile range (IQR)31.9934

Descriptive statistics

Standard deviation14.747376
Coefficient of variation (CV)0.61451478
Kurtosis-1.2254323
Mean23.998407
Median Absolute Deviation (MAD)14.9967
Skewness-0.25869896
Sum1290130.4
Variance217.4851
MonotonicityNot monotonic
2024-06-01T20:41:36.328021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002 310
 
0.6%
0.0004 308
 
0.6%
0.0026 293
 
0.5%
0.0029 285
 
0.5%
0.0022 281
 
0.5%
0.0002 278
 
0.5%
0.0027 277
 
0.5%
0.0013 274
 
0.5%
0.0016 273
 
0.5%
0.0007 273
 
0.5%
Other values (526) 50907
94.7%
ValueCountFrequency (%)
0 145
0.3%
0.0001 267
0.5%
0.0002 278
0.5%
0.0003 265
0.5%
0.0004 308
0.6%
0.0005 257
0.5%
0.0006 259
0.5%
0.0007 273
0.5%
0.0008 262
0.5%
0.0009 242
0.5%
ValueCountFrequency (%)
42.008 62
0.1%
42.0079 140
0.3%
42.0078 132
0.2%
42.0077 140
0.3%
42.0076 150
0.3%
42.0075 146
0.3%
42.0074 131
0.2%
42.0073 117
0.2%
42.0072 119
0.2%
42.0071 133
0.2%

Operation Setting 2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57205635
Minimum0
Maximum0.842
Zeros4187
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:36.513590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2507
median0.7
Q30.84
95-th percentile0.8415
Maximum0.842
Range0.842
Interquartile range (IQR)0.5893

Descriptive statistics

Standard deviation0.31001598
Coefficient of variation (CV)0.54193259
Kurtosis-0.86747978
Mean0.57205635
Median Absolute Deviation (MAD)0.14
Skewness-0.83490945
Sum30753.177
Variance0.09610991
MonotonicityNot monotonic
2024-06-01T20:41:36.683310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.84 11106
20.7%
0 4187
 
7.8%
0.25 4138
 
7.7%
0.62 4128
 
7.7%
0.7 4097
 
7.6%
0.8403 572
 
1.1%
0.8401 558
 
1.0%
0.8414 557
 
1.0%
0.8418 552
 
1.0%
0.8404 548
 
1.0%
Other values (95) 23316
43.4%
ValueCountFrequency (%)
0 4187
7.8%
0.0001 190
 
0.4%
0.0002 225
 
0.4%
0.0003 187
 
0.3%
0.0004 196
 
0.4%
0.0005 176
 
0.3%
0.0006 196
 
0.4%
0.0007 177
 
0.3%
0.0008 220
 
0.4%
0.0009 188
 
0.3%
ValueCountFrequency (%)
0.842 245
0.5%
0.8419 502
0.9%
0.8418 552
1.0%
0.8417 497
0.9%
0.8416 544
1.0%
0.8415 529
1.0%
0.8414 557
1.0%
0.8413 545
1.0%
0.8412 515
1.0%
0.8411 520
1.0%

Operation Setting 3
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
100.0
45757 
60.0
8002 

Length

Max length5
Median length5
Mean length4.8511505
Min length4

Characters and Unicode

Total characters260793
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row60.0
4th row100.0
5th row60.0

Common Values

ValueCountFrequency (%)
100.0 45757
85.1%
60.0 8002
 
14.9%

Length

2024-06-01T20:41:36.844174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:41:36.949287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 45757
85.1%
60.0 8002
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 153275
58.8%
. 53759
 
20.6%
1 45757
 
17.5%
6 8002
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 260793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 153275
58.8%
. 53759
 
20.6%
1 45757
 
17.5%
6 8002
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 260793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 153275
58.8%
. 53759
 
20.6%
1 45757
 
17.5%
6 8002
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 260793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 153275
58.8%
. 53759
 
20.6%
1 45757
 
17.5%
6 8002
 
3.1%

Sensor Measure 1
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.91021
Minimum445
Maximum518.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:37.037657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum445
5-th percentile445
Q1445
median462.54
Q3491.19
95-th percentile518.67
Maximum518.67
Range73.67
Interquartile range (IQR)46.19

Descriptive statistics

Standard deviation26.389707
Coefficient of variation (CV)0.055802786
Kurtosis-1.1810244
Mean472.91021
Median Absolute Deviation (MAD)17.54
Skewness0.45447077
Sum25423180
Variance696.41665
MonotonicityNot monotonic
2024-06-01T20:41:37.150455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
445 13458
25.0%
491.19 8122
15.1%
489.05 8096
15.1%
518.67 8044
15.0%
449.44 8037
15.0%
462.54 8002
14.9%
ValueCountFrequency (%)
445 13458
25.0%
449.44 8037
15.0%
462.54 8002
14.9%
489.05 8096
15.1%
491.19 8122
15.1%
518.67 8044
15.0%
ValueCountFrequency (%)
518.67 8044
15.0%
491.19 8122
15.1%
489.05 8096
15.1%
462.54 8002
14.9%
449.44 8037
15.0%
445 13458
25.0%

Sensor Measure 2
Real number (ℝ)

HIGH CORRELATION 

Distinct1590
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean579.6724
Minimum535.53
Maximum644.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:37.295351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum535.53
5-th percentile536.71
Q1549.57
median555.98
Q3607.34
95-th percentile642.86
Maximum644.52
Range108.99
Interquartile range (IQR)57.77

Descriptive statistics

Standard deviation37.289399
Coefficient of variation (CV)0.064328402
Kurtosis-1.3125035
Mean579.6724
Median Absolute Deviation (MAD)19.28
Skewness0.43747539
Sum31162609
Variance1390.4993
MonotonicityNot monotonic
2024-06-01T20:41:37.448544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
549.7 146
 
0.3%
549.55 144
 
0.3%
549.65 138
 
0.3%
549.69 138
 
0.3%
549.53 134
 
0.2%
549.71 133
 
0.2%
549.57 132
 
0.2%
549.61 132
 
0.2%
549.52 129
 
0.2%
549.42 129
 
0.2%
Other values (1580) 52404
97.5%
ValueCountFrequency (%)
535.53 1
< 0.1%
535.68 1
< 0.1%
535.71 1
< 0.1%
535.78 2
< 0.1%
535.79 1
< 0.1%
535.8 2
< 0.1%
535.81 1
< 0.1%
535.82 2
< 0.1%
535.83 1
< 0.1%
535.84 1
< 0.1%
ValueCountFrequency (%)
644.52 1
 
< 0.1%
644.41 1
 
< 0.1%
644.38 1
 
< 0.1%
644.34 1
 
< 0.1%
644.27 2
< 0.1%
644.26 1
 
< 0.1%
644.25 1
 
< 0.1%
644.22 2
< 0.1%
644.16 1
 
< 0.1%
644.15 4
< 0.1%

Sensor Measure 3
Real number (ℝ)

HIGH CORRELATION 

Distinct12305
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1419.971
Minimum1243.73
Maximum1612.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:37.593185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1243.73
5-th percentile1260.24
Q11352.76
median1369.18
Q31499.37
95-th percentile1592.671
Maximum1612.88
Range369.15
Interquartile range (IQR)146.61

Descriptive statistics

Standard deviation105.94634
Coefficient of variation (CV)0.074611622
Kurtosis-1.1345419
Mean1419.971
Median Absolute Deviation (MAD)108.17
Skewness0.16438574
Sum76336222
Variance11224.627
MonotonicityNot monotonic
2024-06-01T20:41:37.754246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1352.4 22
 
< 0.1%
1363.07 21
 
< 0.1%
1354.14 19
 
< 0.1%
1354.19 19
 
< 0.1%
1353.67 19
 
< 0.1%
1356.02 18
 
< 0.1%
1362.52 18
 
< 0.1%
1359.52 18
 
< 0.1%
1357.46 18
 
< 0.1%
1356.82 18
 
< 0.1%
Other values (12295) 53569
99.6%
ValueCountFrequency (%)
1243.73 1
< 0.1%
1244.98 1
< 0.1%
1245.81 1
< 0.1%
1246.72 1
< 0.1%
1247.01 1
< 0.1%
1247.06 1
< 0.1%
1247.19 1
< 0.1%
1247.75 1
< 0.1%
1247.76 1
< 0.1%
1247.79 1
< 0.1%
ValueCountFrequency (%)
1612.88 1
< 0.1%
1612.34 1
< 0.1%
1610.87 1
< 0.1%
1610.63 1
< 0.1%
1610.11 1
< 0.1%
1609.77 1
< 0.1%
1609.68 1
< 0.1%
1609.54 1
< 0.1%
1609.46 1
< 0.1%
1609.27 1
< 0.1%

Sensor Measure 4
Real number (ℝ)

HIGH CORRELATION 

Distinct15411
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1205.442
Minimum1023.77
Maximum1439.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:37.898702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1023.77
5-th percentile1047.02
Q11123.655
median1138.89
Q31306.85
95-th percentile1411.76
Maximum1439.23
Range415.46
Interquartile range (IQR)183.195

Descriptive statistics

Standard deviation119.12343
Coefficient of variation (CV)0.098821366
Kurtosis-1.1065819
Mean1205.442
Median Absolute Deviation (MAD)92
Skewness0.42889459
Sum64803358
Variance14190.391
MonotonicityNot monotonic
2024-06-01T20:41:38.036252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1127.17 24
 
< 0.1%
1129.76 23
 
< 0.1%
1128.07 22
 
< 0.1%
1123.63 22
 
< 0.1%
1132.93 21
 
< 0.1%
1128.97 21
 
< 0.1%
1128.98 20
 
< 0.1%
1126.17 20
 
< 0.1%
1123.23 20
 
< 0.1%
1126.21 20
 
< 0.1%
Other values (15401) 53546
99.6%
ValueCountFrequency (%)
1023.77 1
< 0.1%
1030.08 1
< 0.1%
1030.12 1
< 0.1%
1030.68 1
< 0.1%
1030.72 1
< 0.1%
1030.92 1
< 0.1%
1032.02 1
< 0.1%
1032.13 1
< 0.1%
1032.26 1
< 0.1%
1032.45 1
< 0.1%
ValueCountFrequency (%)
1439.23 1
< 0.1%
1438.16 1
< 0.1%
1437.9 1
< 0.1%
1436.79 1
< 0.1%
1436.4 1
< 0.1%
1436.26 1
< 0.1%
1436.01 1
< 0.1%
1436 1
< 0.1%
1435.71 1
< 0.1%
1435.69 1
< 0.1%

Sensor Measure 5
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0319861
Minimum3.91
Maximum14.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:38.151678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.91
5-th percentile3.91
Q13.91
median7.05
Q310.52
95-th percentile14.62
Maximum14.62
Range10.71
Interquartile range (IQR)6.61

Descriptive statistics

Standard deviation3.6138388
Coefficient of variation (CV)0.44993091
Kurtosis-0.84908472
Mean8.0319861
Median Absolute Deviation (MAD)3.14
Skewness0.53590548
Sum431791.54
Variance13.059831
MonotonicityNot monotonic
2024-06-01T20:41:38.272649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3.91 13458
25.0%
9.35 8122
15.1%
10.52 8096
15.1%
14.62 8044
15.0%
5.48 8037
15.0%
7.05 8002
14.9%
ValueCountFrequency (%)
3.91 13458
25.0%
5.48 8037
15.0%
7.05 8002
14.9%
9.35 8122
15.1%
10.52 8096
15.1%
14.62 8044
15.0%
ValueCountFrequency (%)
14.62 8044
15.0%
10.52 8096
15.1%
9.35 8122
15.1%
7.05 8002
14.9%
5.48 8037
15.0%
3.91 13458
25.0%

Sensor Measure 6
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.600746
Minimum5.71
Maximum21.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:38.382906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5.71
5-th percentile5.71
Q15.72
median9.03
Q315.49
95-th percentile21.61
Maximum21.61
Range15.9
Interquartile range (IQR)9.77

Descriptive statistics

Standard deviation5.4318016
Coefficient of variation (CV)0.46822865
Kurtosis-0.85066519
Mean11.600746
Median Absolute Deviation (MAD)3.32
Skewness0.62213803
Sum623644.5
Variance29.504469
MonotonicityNot monotonic
2024-06-01T20:41:38.527591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
21.61 7908
14.7%
5.72 7859
14.6%
8 7799
14.5%
9.03 6202
11.5%
5.71 5599
10.4%
15.49 5436
10.1%
13.66 5375
10.0%
13.65 2745
 
5.1%
15.5 2660
 
4.9%
9.02 1796
 
3.3%
Other values (4) 380
 
0.7%
ValueCountFrequency (%)
5.71 5599
10.4%
5.72 7859
14.6%
8 7799
14.5%
8.01 238
 
0.4%
9.02 1796
 
3.3%
9.03 6202
11.5%
9.04 4
 
< 0.1%
13.65 2745
 
5.1%
13.66 5375
10.0%
13.67 2
 
< 0.1%
ValueCountFrequency (%)
21.61 7908
14.7%
21.6 136
 
0.3%
15.5 2660
 
4.9%
15.49 5436
10.1%
13.67 2
 
< 0.1%
13.66 5375
10.0%
13.65 2745
 
5.1%
9.04 4
 
< 0.1%
9.03 6202
11.5%
9.02 1796
 
3.3%

Sensor Measure 7
Real number (ℝ)

HIGH CORRELATION 

Distinct2067
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.60679
Minimum136.8
Maximum555.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:38.667128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum136.8
5-th percentile138.25
Q1139.935
median194.66
Q3394.08
95-th percentile553.79
Maximum555.82
Range419.02
Interquartile range (IQR)254.145

Descriptive statistics

Standard deviation146.00531
Coefficient of variation (CV)0.51663765
Kurtosis-0.87642236
Mean282.60679
Median Absolute Deviation (MAD)56.42
Skewness0.69666673
Sum15192658
Variance21317.549
MonotonicityNot monotonic
2024-06-01T20:41:38.827650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.61 146
 
0.3%
138.56 139
 
0.3%
138.6 138
 
0.3%
138.81 136
 
0.3%
138.58 130
 
0.2%
138.42 129
 
0.2%
138.68 127
 
0.2%
138.72 127
 
0.2%
138.52 127
 
0.2%
138.63 126
 
0.2%
Other values (2057) 52434
97.5%
ValueCountFrequency (%)
136.8 1
< 0.1%
136.99 1
< 0.1%
137.11 1
< 0.1%
137.14 1
< 0.1%
137.15 2
< 0.1%
137.18 2
< 0.1%
137.19 1
< 0.1%
137.2 1
< 0.1%
137.22 1
< 0.1%
137.26 1
< 0.1%
ValueCountFrequency (%)
555.82 2
< 0.1%
555.59 1
< 0.1%
555.56 1
< 0.1%
555.54 2
< 0.1%
555.49 1
< 0.1%
555.45 1
< 0.1%
555.43 1
< 0.1%
555.42 1
< 0.1%
555.41 1
< 0.1%
555.39 1
< 0.1%

Sensor Measure 8
Real number (ℝ)

HIGH CORRELATION 

Distinct897
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2228.8792
Minimum1914.77
Maximum2388.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:38.972399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1914.77
5-th percentile1915.3
Q12211.88
median2223.07
Q32323.96
95-th percentile2388.13
Maximum2388.39
Range473.62
Interquartile range (IQR)112.08

Descriptive statistics

Standard deviation145.20982
Coefficient of variation (CV)0.065149254
Kurtosis0.49908063
Mean2228.8792
Median Absolute Deviation (MAD)95.92
Skewness-1.2221101
Sum1.1982232 × 108
Variance21085.891
MonotonicityNot monotonic
2024-06-01T20:41:39.129203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2323.99 459
 
0.9%
2388.06 455
 
0.8%
2388.09 448
 
0.8%
2323.95 443
 
0.8%
2323.96 442
 
0.8%
2388.08 442
 
0.8%
2323.98 442
 
0.8%
2323.97 438
 
0.8%
2388.13 436
 
0.8%
2388.07 436
 
0.8%
Other values (887) 49318
91.7%
ValueCountFrequency (%)
1914.77 1
 
< 0.1%
1914.78 1
 
< 0.1%
1914.79 2
< 0.1%
1914.82 1
 
< 0.1%
1914.83 2
< 0.1%
1914.85 2
< 0.1%
1914.86 1
 
< 0.1%
1914.87 4
< 0.1%
1914.88 2
< 0.1%
1914.89 4
< 0.1%
ValueCountFrequency (%)
2388.39 1
 
< 0.1%
2388.36 2
 
< 0.1%
2388.35 1
 
< 0.1%
2388.34 5
 
< 0.1%
2388.33 8
 
< 0.1%
2388.32 16
< 0.1%
2388.31 14
 
< 0.1%
2388.3 25
< 0.1%
2388.29 22
< 0.1%
2388.28 35
0.1%

Sensor Measure 9
Real number (ℝ)

HIGH CORRELATION 

Distinct22434
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8525.2008
Minimum7985.56
Maximum9215.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:39.310074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum7985.56
5-th percentile8009.17
Q18321.66
median8361.2
Q38778.03
95-th percentile9064.341
Maximum9215.66
Range1230.1
Interquartile range (IQR)456.37

Descriptive statistics

Standard deviation335.81201
Coefficient of variation (CV)0.03939051
Kurtosis-1.0962285
Mean8525.2008
Median Absolute Deviation (MAD)350.94
Skewness0.12933397
Sum4.5830627 × 108
Variance112769.71
MonotonicityNot monotonic
2024-06-01T20:41:39.519262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8356 13
 
< 0.1%
8325.08 13
 
< 0.1%
8723.85 13
 
< 0.1%
8328.22 12
 
< 0.1%
8008.46 12
 
< 0.1%
8319.11 12
 
< 0.1%
8327.12 12
 
< 0.1%
8322.18 12
 
< 0.1%
8316.61 11
 
< 0.1%
8328.05 11
 
< 0.1%
Other values (22424) 53638
99.8%
ValueCountFrequency (%)
7985.56 1
< 0.1%
7988.08 1
< 0.1%
7989.12 1
< 0.1%
7989.23 1
< 0.1%
7990.09 1
< 0.1%
7990.32 1
< 0.1%
7990.36 1
< 0.1%
7990.6 1
< 0.1%
7990.89 1
< 0.1%
7991.06 1
< 0.1%
ValueCountFrequency (%)
9215.66 1
< 0.1%
9196.29 1
< 0.1%
9194.59 1
< 0.1%
9194.55 1
< 0.1%
9193.96 1
< 0.1%
9187.47 1
< 0.1%
9186 1
< 0.1%
9184.36 1
< 0.1%
9184.35 1
< 0.1%
9184.23 1
< 0.1%

Sensor Measure 10
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0949616
Minimum0.93
Maximum1.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:39.671572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.93
5-th percentile0.94
Q11.02
median1.02
Q31.26
95-th percentile1.3
Maximum1.3
Range0.37
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.12746885
Coefficient of variation (CV)0.11641399
Kurtosis-1.2019464
Mean1.0949616
Median Absolute Deviation (MAD)0.06
Skewness0.60084575
Sum58864.04
Variance0.016248307
MonotonicityNot monotonic
2024-06-01T20:41:39.799655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1.02 20852
38.8%
1.26 8093
 
15.1%
1.3 8044
 
15.0%
0.94 7939
 
14.8%
1.08 6279
 
11.7%
1.07 1843
 
3.4%
1.03 643
 
1.2%
0.93 63
 
0.1%
1.25 3
 
< 0.1%
ValueCountFrequency (%)
0.93 63
 
0.1%
0.94 7939
 
14.8%
1.02 20852
38.8%
1.03 643
 
1.2%
1.07 1843
 
3.4%
1.08 6279
 
11.7%
1.25 3
 
< 0.1%
1.26 8093
 
15.1%
1.3 8044
 
15.0%
ValueCountFrequency (%)
1.3 8044
 
15.0%
1.26 8093
 
15.1%
1.25 3
 
< 0.1%
1.08 6279
 
11.7%
1.07 1843
 
3.4%
1.03 643
 
1.2%
1.02 20852
38.8%
0.94 7939
 
14.8%
0.93 63
 
0.1%

Sensor Measure 11
Real number (ℝ)

HIGH CORRELATION 

Distinct681
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.985172
Minimum36.23
Maximum48.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:39.936301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum36.23
5-th percentile36.7
Q141.91
median42.39
Q345.35
95-th percentile47.63
Maximum48.51
Range12.28
Interquartile range (IQR)3.44

Descriptive statistics

Standard deviation3.2323721
Coefficient of variation (CV)0.075197373
Kurtosis-0.32108848
Mean42.985172
Median Absolute Deviation (MAD)2.14
Skewness-0.54559291
Sum2310839.9
Variance10.448229
MonotonicityNot monotonic
2024-06-01T20:41:40.081183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.97 379
 
0.7%
42.04 372
 
0.7%
42.07 371
 
0.7%
41.99 369
 
0.7%
41.92 363
 
0.7%
42.03 357
 
0.7%
41.96 355
 
0.7%
42.02 354
 
0.7%
42.01 354
 
0.7%
41.94 353
 
0.7%
Other values (671) 50132
93.3%
ValueCountFrequency (%)
36.23 1
 
< 0.1%
36.24 1
 
< 0.1%
36.25 1
 
< 0.1%
36.26 2
< 0.1%
36.27 1
 
< 0.1%
36.28 2
< 0.1%
36.29 4
< 0.1%
36.3 3
< 0.1%
36.31 3
< 0.1%
36.32 3
< 0.1%
ValueCountFrequency (%)
48.51 1
 
< 0.1%
48.41 1
 
< 0.1%
48.4 1
 
< 0.1%
48.38 1
 
< 0.1%
48.37 1
 
< 0.1%
48.36 1
 
< 0.1%
48.35 1
 
< 0.1%
48.34 3
< 0.1%
48.33 1
 
< 0.1%
48.32 2
< 0.1%

Sensor Measure 12
Real number (ℝ)

HIGH CORRELATION 

Distinct1672
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.06903
Minimum129.12
Maximum523.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:40.217611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum129.12
5-th percentile130.27
Q1131.52
median183.2
Q3371.26
95-th percentile521.78
Maximum523.37
Range394.25
Interquartile range (IQR)239.74

Descriptive statistics

Standard deviation137.65951
Coefficient of variation (CV)0.51738267
Kurtosis-0.87566788
Mean266.06903
Median Absolute Deviation (MAD)52.94
Skewness0.69786938
Sum14303605
Variance18950.14
MonotonicityNot monotonic
2024-06-01T20:41:40.394685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.57 174
 
0.3%
130.45 172
 
0.3%
130.59 169
 
0.3%
130.41 168
 
0.3%
130.65 166
 
0.3%
130.56 165
 
0.3%
130.52 163
 
0.3%
130.63 163
 
0.3%
130.71 161
 
0.3%
130.74 159
 
0.3%
Other values (1662) 52099
96.9%
ValueCountFrequency (%)
129.12 1
 
< 0.1%
129.24 1
 
< 0.1%
129.27 1
 
< 0.1%
129.31 1
 
< 0.1%
129.32 1
 
< 0.1%
129.33 1
 
< 0.1%
129.39 1
 
< 0.1%
129.4 1
 
< 0.1%
129.42 2
 
< 0.1%
129.45 5
< 0.1%
ValueCountFrequency (%)
523.37 1
< 0.1%
523.32 1
< 0.1%
523.29 1
< 0.1%
523.18 1
< 0.1%
523.14 1
< 0.1%
523.13 1
< 0.1%
523.12 1
< 0.1%
523.11 1
< 0.1%
523.1 1
< 0.1%
523.09 1
< 0.1%

Sensor Measure 13
Real number (ℝ)

HIGH CORRELATION 

Distinct514
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2334.5573
Minimum2027.61
Maximum2390.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:40.563863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2027.61
5-th percentile2028.2
Q12387.9
median2388.08
Q32388.17
95-th percentile2388.4
Maximum2390.48
Range362.87
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation128.06827
Coefficient of variation (CV)0.054857627
Kurtosis1.8933561
Mean2334.5573
Median Absolute Deviation (MAD)0.11
Skewness-1.9731341
Sum1.2550346 × 108
Variance16401.482
MonotonicityNot monotonic
2024-06-01T20:41:40.732810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.11 1602
 
3.0%
2388.08 1592
 
3.0%
2388.12 1588
 
3.0%
2388.1 1571
 
2.9%
2388.09 1560
 
2.9%
2388.07 1493
 
2.8%
2388.13 1449
 
2.7%
2388.14 1441
 
2.7%
2388.06 1424
 
2.6%
2388.15 1376
 
2.6%
Other values (504) 38663
71.9%
ValueCountFrequency (%)
2027.61 1
 
< 0.1%
2027.64 1
 
< 0.1%
2027.65 1
 
< 0.1%
2027.66 1
 
< 0.1%
2027.67 2
< 0.1%
2027.69 2
< 0.1%
2027.71 1
 
< 0.1%
2027.72 1
 
< 0.1%
2027.73 3
< 0.1%
2027.74 3
< 0.1%
ValueCountFrequency (%)
2390.48 1
< 0.1%
2390.39 1
< 0.1%
2390.38 1
< 0.1%
2390.32 1
< 0.1%
2390.27 1
< 0.1%
2390.24 1
< 0.1%
2390.23 1
< 0.1%
2390.21 1
< 0.1%
2390.2 1
< 0.1%
2390.19 1
< 0.1%

Sensor Measure 14
Real number (ℝ)

HIGH CORRELATION 

Distinct14905
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8066.5977
Minimum7848.36
Maximum8268.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:40.884951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum7848.36
5-th percentile7872.769
Q18062.14
median8082.54
Q38127.195
95-th percentile8149.442
Maximum8268.5
Range420.14
Interquartile range (IQR)65.055

Descriptive statistics

Standard deviation84.83795
Coefficient of variation (CV)0.010517191
Kurtosis0.93587119
Mean8066.5977
Median Absolute Deviation (MAD)28.89
Skewness-1.4079677
Sum4.3365222 × 108
Variance7197.4777
MonotonicityNot monotonic
2024-06-01T20:41:41.046613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8066.94 17
 
< 0.1%
8075.98 16
 
< 0.1%
8083.52 16
 
< 0.1%
8063.87 15
 
< 0.1%
8070.18 15
 
< 0.1%
8134.86 15
 
< 0.1%
8138.29 15
 
< 0.1%
8070.23 15
 
< 0.1%
8079.85 15
 
< 0.1%
8073.41 15
 
< 0.1%
Other values (14895) 53605
99.7%
ValueCountFrequency (%)
7848.36 1
< 0.1%
7848.43 1
< 0.1%
7849.26 1
< 0.1%
7850.23 1
< 0.1%
7850.96 1
< 0.1%
7851.03 1
< 0.1%
7851.8 1
< 0.1%
7852.44 1
< 0.1%
7852.71 1
< 0.1%
7853.11 1
< 0.1%
ValueCountFrequency (%)
8268.5 1
< 0.1%
8259.23 1
< 0.1%
8257.42 1
< 0.1%
8252.61 1
< 0.1%
8251.88 1
< 0.1%
8251.31 1
< 0.1%
8248.92 1
< 0.1%
8248.19 1
< 0.1%
8246.81 1
< 0.1%
8246.7 1
< 0.1%

Sensor Measure 15
Real number (ℝ)

HIGH CORRELATION 

Distinct8464
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3296535
Minimum8.3357
Maximum11.0669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:41.175980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8.3357
5-th percentile8.4236
Q18.6778
median9.3109
Q39.3869
95-th percentile10.927
Maximum11.0669
Range2.7312
Interquartile range (IQR)0.7091

Descriptive statistics

Standard deviation0.74933549
Coefficient of variation (CV)0.080317612
Kurtosis0.38080056
Mean9.3296535
Median Absolute Deviation (MAD)0.1154
Skewness1.0536468
Sum501552.84
Variance0.56150368
MonotonicityNot monotonic
2024-06-01T20:41:41.321604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.3464 33
 
0.1%
9.3415 33
 
0.1%
9.3601 33
 
0.1%
9.37 32
 
0.1%
9.355 32
 
0.1%
9.3767 31
 
0.1%
9.3592 31
 
0.1%
9.3529 30
 
0.1%
9.351 30
 
0.1%
9.3716 29
 
0.1%
Other values (8454) 53445
99.4%
ValueCountFrequency (%)
8.3357 1
< 0.1%
8.3411 1
< 0.1%
8.3413 1
< 0.1%
8.3437 1
< 0.1%
8.3461 1
< 0.1%
8.3472 1
< 0.1%
8.3475 1
< 0.1%
8.3483 1
< 0.1%
8.3493 1
< 0.1%
8.3505 1
< 0.1%
ValueCountFrequency (%)
11.0669 1
< 0.1%
11.0657 1
< 0.1%
11.0645 1
< 0.1%
11.0642 1
< 0.1%
11.0547 1
< 0.1%
11.0545 1
< 0.1%
11.0537 2
< 0.1%
11.0512 1
< 0.1%
11.0499 1
< 0.1%
11.0454 1
< 0.1%

Sensor Measure 16
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
0.02
35879 
0.03
17880 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters215036
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.02
2nd row0.02
3rd row0.02
4th row0.02
5th row0.02

Common Values

ValueCountFrequency (%)
0.02 35879
66.7%
0.03 17880
33.3%

Length

2024-06-01T20:41:41.450483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:41:41.546917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02 35879
66.7%
0.03 17880
33.3%

Most occurring characters

ValueCountFrequency (%)
0 107518
50.0%
. 53759
25.0%
2 35879
 
16.7%
3 17880
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 215036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 107518
50.0%
. 53759
25.0%
2 35879
 
16.7%
3 17880
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 215036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 107518
50.0%
. 53759
25.0%
2 35879
 
16.7%
3 17880
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 215036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 107518
50.0%
. 53759
25.0%
2 35879
 
16.7%
3 17880
 
8.3%

Sensor Measure 17
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348.30951
Minimum303
Maximum399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:41.686455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum303
5-th percentile307
Q1331
median335
Q3369
95-th percentile394
Maximum399
Range96
Interquartile range (IQR)38

Descriptive statistics

Standard deviation27.754515
Coefficient of variation (CV)0.079683484
Kurtosis-1.1319951
Mean348.30951
Median Absolute Deviation (MAD)28
Skewness0.17996401
Sum18724771
Variance770.31313
MonotonicityNot monotonic
2024-06-01T20:41:41.893639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331 4027
 
7.5%
332 3269
 
6.1%
330 3245
 
6.0%
333 3094
 
5.8%
334 2913
 
5.4%
307 2473
 
4.6%
369 2369
 
4.4%
365 2247
 
4.2%
393 2195
 
4.1%
308 2006
 
3.7%
Other values (43) 25921
48.2%
ValueCountFrequency (%)
303 2
 
< 0.1%
304 50
 
0.1%
305 444
 
0.8%
306 1576
2.9%
307 2473
4.6%
308 2006
3.7%
309 1041
1.9%
310 328
 
0.6%
311 75
 
0.1%
312 6
 
< 0.1%
ValueCountFrequency (%)
399 1
 
< 0.1%
398 34
 
0.1%
397 164
 
0.3%
396 421
 
0.8%
395 886
1.6%
394 1597
3.0%
393 2195
4.1%
392 1806
3.4%
391 769
 
1.4%
390 162
 
0.3%

Sensor Measure 18
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2228.8064
Minimum1915
Maximum2388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:42.053578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1915
5-th percentile1915
Q12212
median2223
Q32324
95-th percentile2388
Maximum2388
Range473
Interquartile range (IQR)112

Descriptive statistics

Standard deviation145.32798
Coefficient of variation (CV)0.065204399
Kurtosis0.50184604
Mean2228.8064
Median Absolute Deviation (MAD)96
Skewness-1.223926
Sum1.198184 × 108
Variance21120.222
MonotonicityNot monotonic
2024-06-01T20:41:42.189676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2212 13458
25.0%
2324 8122
15.1%
2319 8096
15.1%
2388 8044
15.0%
2223 8037
15.0%
1915 8002
14.9%
ValueCountFrequency (%)
1915 8002
14.9%
2212 13458
25.0%
2223 8037
15.0%
2319 8096
15.1%
2324 8122
15.1%
2388 8044
15.0%
ValueCountFrequency (%)
2388 8044
15.0%
2324 8122
15.1%
2319 8096
15.1%
2223 8037
15.0%
2212 13458
25.0%
1915 8002
14.9%

Sensor Measure 19
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
100.0
45757 
84.93
8002 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters268795
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row84.93
4th row100.0
5th row84.93

Common Values

ValueCountFrequency (%)
100.0 45757
85.1%
84.93 8002
 
14.9%

Length

2024-06-01T20:41:42.312253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:41:42.408667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 45757
85.1%
84.93 8002
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 137271
51.1%
. 53759
 
20.0%
1 45757
 
17.0%
8 8002
 
3.0%
4 8002
 
3.0%
9 8002
 
3.0%
3 8002
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 268795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 137271
51.1%
. 53759
 
20.0%
1 45757
 
17.0%
8 8002
 
3.0%
4 8002
 
3.0%
9 8002
 
3.0%
3 8002
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 268795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 137271
51.1%
. 53759
 
20.0%
1 45757
 
17.0%
8 8002
 
3.0%
4 8002
 
3.0%
9 8002
 
3.0%
3 8002
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 268795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 137271
51.1%
. 53759
 
20.0%
1 45757
 
17.0%
8 8002
 
3.0%
4 8002
 
3.0%
9 8002
 
3.0%
3 8002
 
3.0%

Sensor Measure 20
Real number (ℝ)

HIGH CORRELATION 

Distinct510
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.789296
Minimum10.18
Maximum39.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:42.569762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10.18
5-th percentile10.5
Q110.91
median14.88
Q328.47
95-th percentile38.91
Maximum39.34
Range29.16
Interquartile range (IQR)17.56

Descriptive statistics

Standard deviation9.8693312
Coefficient of variation (CV)0.47473138
Kurtosis-0.93847649
Mean20.789296
Median Absolute Deviation (MAD)4.39
Skewness0.64004021
Sum1117611.8
Variance97.403698
MonotonicityNot monotonic
2024-06-01T20:41:42.754479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.59 515
 
1.0%
10.6 504
 
0.9%
10.61 500
 
0.9%
10.58 488
 
0.9%
10.57 481
 
0.9%
10.55 472
 
0.9%
10.62 471
 
0.9%
10.56 462
 
0.9%
10.53 461
 
0.9%
10.64 461
 
0.9%
Other values (500) 48944
91.0%
ValueCountFrequency (%)
10.18 1
 
< 0.1%
10.19 1
 
< 0.1%
10.2 1
 
< 0.1%
10.21 1
 
< 0.1%
10.22 1
 
< 0.1%
10.23 3
 
< 0.1%
10.24 3
 
< 0.1%
10.26 4
 
< 0.1%
10.27 5
< 0.1%
10.28 12
< 0.1%
ValueCountFrequency (%)
39.34 1
 
< 0.1%
39.32 1
 
< 0.1%
39.31 1
 
< 0.1%
39.27 4
 
< 0.1%
39.26 4
 
< 0.1%
39.25 5
< 0.1%
39.24 5
< 0.1%
39.23 4
 
< 0.1%
39.22 10
< 0.1%
39.21 10
< 0.1%

Sensor Measure 21
Real number (ℝ)

HIGH CORRELATION 

Distinct17837
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.473423
Minimum6.0105
Maximum23.5901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:42.898797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6.0105
5-th percentile6.2976
Q16.5463
median8.9292
Q317.0832
95-th percentile23.3424
Maximum23.5901
Range17.5796
Interquartile range (IQR)10.5369

Descriptive statistics

Standard deviation5.9216152
Coefficient of variation (CV)0.4747386
Kurtosis-0.93862637
Mean12.473423
Median Absolute Deviation (MAD)2.6327
Skewness0.63992468
Sum670558.73
Variance35.065527
MonotonicityNot monotonic
2024-06-01T20:41:43.043532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3596 18
 
< 0.1%
6.3664 17
 
< 0.1%
6.3493 17
 
< 0.1%
6.335 17
 
< 0.1%
6.3539 15
 
< 0.1%
6.3889 15
 
< 0.1%
6.3561 15
 
< 0.1%
6.3873 15
 
< 0.1%
6.313 15
 
< 0.1%
6.3662 15
 
< 0.1%
Other values (17827) 53600
99.7%
ValueCountFrequency (%)
6.0105 1
< 0.1%
6.0804 1
< 0.1%
6.0918 1
< 0.1%
6.1376 1
< 0.1%
6.1461 1
< 0.1%
6.1509 1
< 0.1%
6.1559 1
< 0.1%
6.1562 1
< 0.1%
6.157 1
< 0.1%
6.1584 1
< 0.1%
ValueCountFrequency (%)
23.5901 1
< 0.1%
23.5755 1
< 0.1%
23.5744 1
< 0.1%
23.5651 1
< 0.1%
23.5613 1
< 0.1%
23.5515 1
< 0.1%
23.5484 2
< 0.1%
23.548 1
< 0.1%
23.5477 1
< 0.1%
23.5474 1
< 0.1%

RUL
Real number (ℝ)

HIGH CORRELATION 

Distinct378
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.15475
Minimum1
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.1 KiB
2024-06-01T20:41:43.183985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q152
median104
Q3157
95-th percentile231
Maximum378
Range377
Interquartile range (IQR)105

Descriptive statistics

Standard deviation69.180569
Coefficient of variation (CV)0.63378434
Kurtosis-0.22701327
Mean109.15475
Median Absolute Deviation (MAD)52
Skewness0.50086424
Sum5868050
Variance4785.9511
MonotonicityNot monotonic
2024-06-01T20:41:43.336836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 260
 
0.5%
39 260
 
0.5%
55 260
 
0.5%
54 260
 
0.5%
53 260
 
0.5%
52 260
 
0.5%
51 260
 
0.5%
50 260
 
0.5%
49 260
 
0.5%
48 260
 
0.5%
Other values (368) 51159
95.2%
ValueCountFrequency (%)
1 260
0.5%
2 260
0.5%
3 260
0.5%
4 260
0.5%
5 260
0.5%
6 260
0.5%
7 260
0.5%
8 260
0.5%
9 260
0.5%
10 260
0.5%
ValueCountFrequency (%)
378 1
< 0.1%
377 1
< 0.1%
376 1
< 0.1%
375 1
< 0.1%
374 1
< 0.1%
373 1
< 0.1%
372 1
< 0.1%
371 1
< 0.1%
370 1
< 0.1%
369 1
< 0.1%

Interactions

2024-06-01T20:41:29.888958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:12.758913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:16.042199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:19.860645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:23.106973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:26.351954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:29.615765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:32.744931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:35.962526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:39.568457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:43.234409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:46.943370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:49.993145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:53.116663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:56.351280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:59.692671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:03.567351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:06.886811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:10.250159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:13.377832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:16.579517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:19.813020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:23.091442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:26.376575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:30.018558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:12.894228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:16.158780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:19.980462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:23.243757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:26.477690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:29.752068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:32.894560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:36.071333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:39.691917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:43.575236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:47.057473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:50.109686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:53.236439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:56.480029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:59.813629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:03.712503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:07.015576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:10.379641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:13.515801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:16.714173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:19.943392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:23.242052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:26.492881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:30.164098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:13.025149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:16.290741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:20.099789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:23.358692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:26.606063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:29.912836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:33.028928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:36.201308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:39.837132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:43.891190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:47.201550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:50.259470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:53.356866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:56.616942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:59.933434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:03.857546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:07.194296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:10.500718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-06-01T20:40:42.142350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:46.279552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:49.325816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:52.484113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:55.663168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:58.943537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:02.922018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:06.188200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:09.556318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:12.721341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:15.868931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:19.164937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:22.433525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:25.693326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:29.198261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:33.680739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:15.489078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:19.353947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:22.550207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:25.787882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:29.076222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:32.245329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:35.387126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:39.091469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:42.285514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:46.417791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:49.445740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:52.596671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:55.786842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:59.088795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:03.047024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:06.351977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:09.686373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:12.858868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:15.981457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:19.286181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:22.547413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:25.824722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:29.341901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:33.810032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:15.621124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:19.481177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:22.665860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:25.925025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:29.213113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:32.366708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:35.546026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:39.219772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:42.511055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:46.537583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:49.566446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:52.701572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:55.935747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:59.230088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:03.167441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:06.488837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:09.817652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:12.996363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:16.119175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:19.436482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:22.703750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:25.954074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:29.477812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:33.930747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:15.775250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:19.606827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:22.798735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:26.043454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:29.358360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:32.490898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:35.684242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:39.347062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:42.809902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:46.675545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:49.715021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:52.857734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:56.063017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:59.418797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:03.305153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:06.625216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:09.962547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:13.120717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:16.274697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:19.544173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:22.835948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:26.091755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:29.604483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:34.106474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:15.920415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:19.724646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:22.951872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:26.166159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:29.481197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:32.615579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:35.801161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:39.463865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:43.022819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:46.804998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:49.853004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:52.986299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:56.204628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:40:59.540362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:03.431059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:06.753970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:10.095872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:13.238811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:16.419731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:19.689446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:22.961545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:26.246930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:41:29.757698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-06-01T20:41:43.489578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CycleOperation Setting 1Operation Setting 2Operation Setting 3RULSensor Measure 1Sensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 16Sensor Measure 17Sensor Measure 18Sensor Measure 19Sensor Measure 2Sensor Measure 20Sensor Measure 21Sensor Measure 3Sensor Measure 4Sensor Measure 5Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9UnitNumber
Cycle1.000-0.001-0.0010.014-0.7830.000-0.0060.146-0.0690.2950.1230.1390.0730.104-0.0030.0140.088-0.066-0.0650.1010.1430.0000.044-0.0660.0670.0890.013
Operation Setting 1-0.0011.0000.8871.0000.002-0.942-0.754-0.709-0.928-0.207-0.4000.7490.941-0.766-0.7431.000-0.730-0.928-0.928-0.765-0.739-0.984-0.974-0.928-0.730-0.763-0.004
Operation Setting 2-0.0010.8871.0001.0000.002-0.818-0.603-0.582-0.772-0.030-0.3830.5780.941-0.580-0.5071.000-0.499-0.772-0.772-0.579-0.582-0.901-0.893-0.772-0.499-0.579-0.005
Operation Setting 30.0141.0001.0001.0000.0040.0380.6390.6170.2540.6170.617-0.6170.2950.6170.6271.0000.6170.2540.2540.6170.6170.0380.0380.2540.6170.6170.005
RUL-0.7830.0020.0020.0041.000-0.0020.006-0.1700.081-0.247-0.074-0.1610.093-0.1190.0010.004-0.1020.0730.072-0.116-0.165-0.002-0.0460.077-0.056-0.0650.013
Sensor Measure 10.000-0.942-0.8180.038-0.0021.0000.7230.6790.9010.1990.264-0.7170.9410.7390.7971.0000.7840.9010.9010.7380.7100.9570.9490.9010.7840.7360.000
Sensor Measure 10-0.006-0.754-0.6030.6390.0060.7231.0000.9560.8320.5190.760-0.9560.9500.9580.9301.0000.9140.8310.8310.9570.9570.7670.7600.8320.9160.9590.004
Sensor Measure 110.146-0.709-0.5820.617-0.1700.6790.9561.0000.7480.5070.748-0.8580.9570.9410.8781.0000.8920.7510.7510.9390.9700.7210.7240.7490.8670.9150.020
Sensor Measure 12-0.069-0.928-0.7720.2540.0810.9010.8320.7481.0000.3250.483-0.8640.9410.8540.8510.5130.8240.9770.9780.8530.8010.9430.9300.9790.8340.865-0.003
Sensor Measure 130.295-0.207-0.0300.617-0.2470.1990.5190.5070.3251.0000.566-0.4950.2950.5480.5141.0000.5330.3150.3160.5460.5290.2100.2380.3220.6220.617-0.006
Sensor Measure 140.123-0.400-0.3830.617-0.0740.2640.7600.7480.4830.5661.000-0.7080.8430.7010.5571.0000.5530.4760.4770.7000.7240.4070.4130.4820.5870.760-0.005
Sensor Measure 150.1390.7490.578-0.617-0.161-0.717-0.956-0.858-0.864-0.495-0.7081.0000.929-0.913-0.9181.000-0.878-0.860-0.860-0.913-0.880-0.761-0.746-0.862-0.902-0.9370.008
Sensor Measure 160.0730.9410.9410.2950.0930.9410.9500.9570.9410.2950.8430.9291.0000.8120.6820.2950.6810.7930.7930.8110.8140.8150.8130.7920.6800.8040.011
Sensor Measure 170.104-0.766-0.5800.617-0.1190.7390.9580.9410.8540.5480.701-0.9130.8121.0000.9381.0000.9420.8560.8560.9790.9670.7790.7790.8550.9300.9650.014
Sensor Measure 18-0.003-0.743-0.5070.6270.0010.7970.9300.8780.8510.5140.557-0.9180.6820.9381.0001.0000.9840.8510.8510.9370.9090.7550.7480.8510.9840.9350.003
Sensor Measure 190.0141.0001.0001.0000.0041.0001.0001.0000.5131.0001.0001.0000.2951.0001.0001.0000.6170.2540.2540.6170.6170.0380.0380.2540.6170.6170.005
Sensor Measure 20.088-0.730-0.4990.617-0.1020.7840.9140.8920.8240.5330.553-0.8780.6810.9420.9840.6171.0000.8260.8250.9410.9220.7430.7420.8250.9730.9270.009
Sensor Measure 20-0.066-0.928-0.7720.2540.0730.9010.8310.7510.9770.3150.476-0.8600.7930.8560.8510.2540.8261.0000.9760.8550.8040.9430.9300.9770.8320.861-0.002
Sensor Measure 21-0.065-0.928-0.7720.2540.0720.9010.8310.7510.9780.3160.477-0.8600.7930.8560.8510.2540.8250.9761.0000.8550.8040.9430.9300.9770.8320.861-0.002
Sensor Measure 30.101-0.765-0.5790.617-0.1160.7380.9570.9390.8530.5460.700-0.9130.8110.9790.9370.6170.9410.8550.8551.0000.9650.7780.7780.8540.9290.9640.013
Sensor Measure 40.143-0.739-0.5820.617-0.1650.7100.9570.9700.8010.5290.724-0.8800.8140.9670.9090.6170.9220.8040.8040.9651.0000.7520.7550.8030.8990.9430.018
Sensor Measure 50.000-0.984-0.9010.038-0.0020.9570.7670.7210.9430.2100.407-0.7610.8150.7790.7550.0380.7430.9430.9430.7780.7521.0000.9910.9430.7430.7760.003
Sensor Measure 60.044-0.974-0.8930.038-0.0460.9490.7600.7240.9300.2380.413-0.7460.8130.7790.7480.0380.7420.9300.9300.7780.7550.9911.0000.9300.7430.7770.005
Sensor Measure 7-0.066-0.928-0.7720.2540.0770.9010.8320.7490.9790.3220.482-0.8620.7920.8550.8510.2540.8250.9770.9770.8540.8030.9430.9301.0000.8340.864-0.003
Sensor Measure 80.067-0.730-0.4990.617-0.0560.7840.9160.8670.8340.6220.587-0.9020.6800.9300.9840.6170.9730.8320.8320.9290.8990.7430.7430.8341.0000.9460.000
Sensor Measure 90.089-0.763-0.5790.617-0.0650.7360.9590.9150.8650.6170.760-0.9370.8040.9650.9350.6170.9270.8610.8610.9640.9430.7760.7770.8640.9461.000-0.000
UnitNumber0.013-0.004-0.0050.0050.0130.0000.0040.020-0.003-0.006-0.0050.0080.0110.0140.0030.0050.009-0.002-0.0020.0130.0180.0030.005-0.0030.000-0.0001.000

Missing values

2024-06-01T20:41:34.302523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-01T20:41:34.825164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UnitNumberCycleOperation Setting 1Operation Setting 2Operation Setting 3Sensor Measure 1Sensor Measure 2Sensor Measure 3Sensor Measure 4Sensor Measure 5Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9Sensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 16Sensor Measure 17Sensor Measure 18Sensor Measure 19Sensor Measure 20Sensor Measure 21RUL
01134.99830.8400100.0449.44555.321358.611137.235.488.00194.642222.658341.911.0242.02183.062387.728048.569.34610.023342223100.0014.738.8071149
11241.99820.8408100.0445.00549.901353.221125.783.915.71138.512211.578303.961.0242.20130.422387.668072.309.37740.023302212100.0010.416.2665148
21324.99880.621860.0462.54537.311256.761047.457.059.02175.711915.118001.420.9436.69164.222028.037864.8710.89410.02309191584.9314.088.6723147
31442.00770.8416100.0445.00549.511354.031126.383.915.71138.462211.588303.961.0241.96130.722387.618068.669.35280.023292212100.0010.596.4701146
41525.00050.620360.0462.54537.071257.711047.937.059.03175.051915.107993.230.9436.89164.312028.007861.2310.89630.02309191584.9314.138.5286145
51625.00450.620560.0462.54537.021266.381048.727.059.03175.171915.157996.100.9436.78164.272028.017868.8710.89120.02306191584.9314.288.5590144
61742.00430.8409100.0445.00549.741347.451127.193.915.71138.712211.628307.751.0242.19130.492387.698075.549.37530.023302212100.0010.626.4227143
71820.00200.7002100.0491.19607.441481.691252.369.3513.65334.412323.878709.121.0844.27315.112387.998049.269.23690.023652324100.0024.3314.7989142
81941.99950.8407100.0445.00549.331348.231127.453.915.71138.062211.618312.331.0242.30130.972387.628065.789.38780.023312212100.0010.696.3802141
911042.00110.8400100.0445.00549.331356.401127.113.915.71138.702211.568302.311.0242.02130.502387.628069.119.39570.023292212100.0010.576.2847140
UnitNumberCycleOperation Setting 1Operation Setting 2Operation Setting 3Sensor Measure 1Sensor Measure 2Sensor Measure 3Sensor Measure 4Sensor Measure 5Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9Sensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 16Sensor Measure 17Sensor Measure 18Sensor Measure 19Sensor Measure 20Sensor Measure 21RUL
537492603070.00010.0000100.0518.67643.231598.431428.3314.6221.61551.752388.129186.001.3048.12519.872388.098237.528.52460.033952388100.0038.8223.113510
5375026030825.00640.620060.0462.54537.001265.571062.837.059.03175.851917.168105.080.9437.22164.212030.107961.4711.06450.02309191584.9314.288.49479
5375126030942.00760.8415100.0445.00550.751372.111144.783.915.72139.482213.758437.701.0342.72130.802389.948191.459.38800.023342212100.0010.596.32418
5375226031010.00080.2513100.0489.05605.351519.891329.3710.5215.50392.552319.178902.521.2646.02370.982388.438240.418.73520.033732319100.0028.3616.94437
537532603110.00150.0010100.0518.67644.151604.811427.5314.6221.61552.022388.139184.231.3048.14520.582388.148246.208.53030.033962388100.0038.4623.12526
5375426031220.00370.7000100.0491.19608.791495.601269.519.3513.66334.002324.928852.271.0744.72314.052389.028169.649.30350.033692324100.0024.3614.51895
5375526031310.00220.2510100.0489.05605.811514.321324.1210.5215.50392.462319.178915.861.2646.08371.222388.428245.368.75860.033742319100.0028.1016.94544
5375626031425.00410.620060.0462.54537.481276.241057.927.059.04175.111917.378114.330.9437.14163.742030.337971.2511.06570.02310191584.9314.198.55033
5375726031525.00330.622060.0462.54537.841272.951066.307.059.04174.811917.368112.630.9437.32164.372030.357972.4711.05370.02311191584.9314.058.37292
5375826031635.00360.8400100.0449.44556.641374.611145.525.488.01194.332225.128478.441.0242.50183.092390.388185.359.39980.023382223100.0014.758.84461